Evidence map›Paper›PMID 36162852›Full record

ArticleJournal of medical genetics2022

Prospective validation of the BOADICEA multifactorial breast cancer risk prediction model in a large prospective cohort study.

Xin Yang, Mikael Eriksson, Kamila Czene, Andrew Lee, Goska Leslie, Michael Lush, Jean Wang, Joe Dennis, Leila Dorling, Sara Carvalho and 6 more

Registry-linked trialOpen access · hybridAbstract read
In one paragraph

Article in Journal of medical genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06922708 (Implementing Polygenic Risk Scores for Breast Cancer Prevention), which is not on this map. Cited by 59 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
59citing papers in PubMed, 4 pooled it
9.0field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT06922708 narecruitingnot on this mapstarted 2025, after this paper: background citation

Implementing Polygenic Risk Scores for Breast Cancer Prevention: Protocol for a Feasibility Study in a Real-world Clinical Setting

TypeinterventionalSponsorCatholic University of the Sacred HeartRan2025 to 2026Enrolled100ConditionsBreast CancerArmsClinical pathway for breast cancer prevention based on PRS-integrated CanRisk assessment
3 · Its place in the literature

Who cites it

59 citing papers in PubMed, 4 syntheses or guidelines pooled it, 92 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Guideline
  4. Pooled it
  5. Trial
  6. Risk-based breast cancer screening.British journal of cancer · 2026
    Article
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  10. Review
  11. Review
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  17. Emerging Nanozyme Strategies for Precision Breast Cancer Treatment.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors at 4 institutions in 4 countries.

Xin YangCentre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK.ORCID 0000-0003-0037-3790
Mikael ErikssonDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Kamila CzeneDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Andrew LeeCentre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK.ORCID 0000-0003-0677-0252
Goska LeslieCentre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK.
Michael LushCentre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK.
Jean WangCentre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK.
Joe DennisCentre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK.
Leila DorlingCentre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK.
Sara CarvalhoCentre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK.
Nasim MavaddatCentre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK.
Jacques SimardDepartment of Molecular Medicine, Université Laval and CHU de Québec-Université Laval Research Center, Quebec City, Quebec, Canada.
Marjanka K SchmidtDepartment of Clinical Genetics, Leiden University Medical Center, Leiden, The Netherlands.
Douglas F EastonCentre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK.
Per HallDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Antonis C AntoniouCentre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK aca20@medschl.cam.ac.uk.ORCID 0000-0001-9223-3116
University of Cambridge · GBKarolinska Institutet · SELeiden University Medical Center · NLUniversité Laval · CA

Funding

Cancer Research UK 20861Cancer Research UK C12292/A20861Cancer Research UK PPRPGM-NOV20\100002Department of Health
6 · The paper itself

Abstract

backgroundThe multifactorial Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) breast cancer risk prediction model has been recently extended to consider all established breast cancer risk factors. We assessed the clinical validity of the model in a large independent prospective cohort.

methodsWe validated BOADICEA (V.6) in the Swedish KARolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA) cohort including 66 415 women of European ancestry (median age 54 years, IQR 45-63; 816 incident breast cancers) without previous cancer diagnosis. We calculated 5-year risks on the basis of questionnaire-based risk factors, pedigree-structured first-degree family history, mammographic density (BI-RADS), a validated breast cancer polygenic risk score (PRS) based on 313-SNPs, and pathogenic variant status in 8 breast cancer susceptibility genes:

resultsAmong the individual model components, the PRS contributed most to breast cancer risk stratification. BOADICEA was well calibrated in predicting the risks for low-risk and high-risk women when all, or subsets of risk factors are included in the risk prediction. Discrimination was maximised when all risk factors are considered (AUC=0.70, 95% CI: 0.66 to 0.73; expected-to-observed ratio=0.88, 95% CI: 0.75 to 1.04; calibration slope=0.97, 95% CI: 0.95 to 0.99). The full multifactorial model classified 3.6% women as high risk (5-year risk ≥3%) and 11.1% as very low risk (5-year risk <0.33%).

conclusionThe multifactorial BOADICEA model provides valid breast cancer risk predictions and a basis for personalised decision-making on disease prevention and screening.

Indexed as

Breast NeoplasmsFemaleGenes, BRCA2Genetic Predisposition to DiseaseHumansMiddle AgedProspective StudiesRisk Assessmentgenetic counselingpublic healthwomen's health

Identifiers

PMID36162852
PMCPMC9691822
OpenAlexW4297238926

What OpenQuestion holds

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LicenceCC BY
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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.